Artificial Intelligence › Core AI ›

Reinforcement Learning

767 directly classified papers

Papers per year

Papers

Interactive Semantic Parsing for If-Then Recipes via Hierarchical Reinforcement Learning AAAI 2019

Deep Reinforcement Learning for Syntactic Error Repair in Student Programs AAAI 2019

Surveys without Questions: A Reinforcement Learning Approach AAAI 2019

On-Policy Robot Imitation Learning from a Converging Supervisor CORL 2019

To Follow or not to Follow: Selective Imitation Learning from Observations CORL 2019

Data Efficient Reinforcement Learning for Legged Robots CORL 2019

Reinforcement-based denoising of distantly supervised NER with partial annotation EMNLP 2019

Human-Like Decision Making: Document-level Aspect Sentiment Classification via Hierarchical Reinforcement Learning EMNLP 2019

Learning to Ask for Conversational Machine Learning EMNLP 2019

DIVINE: A Generative Adversarial Imitation Learning Framework for Knowledge Graph Reasoning EMNLP 2019

Incorporating Graph Attention Mechanism into Knowledge Graph Reasoning Based on Deep Reinforcement Learning EMNLP 2019

Sim-Real Joint Reinforcement Transfer for 3D Indoor Navigation CVPR 2019

Actor-Critic Instance Segmentation CVPR 2019

Scene Memory Transformer for Embodied Agents in Long-Horizon Tasks CVPR 2019

Know More about Each Other: Evolving Dialogue Strategy via Compound Assessment ACL 2019

Multi-Agent Common Knowledge Reinforcement Learning NIPS 2019

Using a Logarithmic Mapping to Enable Lower Discount Factors in Reinforcement Learning NIPS 2019

A neurally plausible model learns successor representations in partially observable environments NIPS 2019

Exploration Bonus for Regret Minimization in Discrete and Continuous Average Reward MDPs NIPS 2019

Unsupervised Curricula for Visual Meta-Reinforcement Learning NIPS 2019

Control What You Can: Intrinsically Motivated Task-Planning Agent NIPS 2019

Variational Temporal Abstraction NIPS 2019

Better Exploration with Optimistic Actor Critic NIPS 2019

Regret Bounds for Learning State Representations in Reinforcement Learning NIPS 2019

Non-Stationary Markov Decision Processes, a Worst-Case Approach using Model-Based Reinforcement Learning NIPS 2019